Skill Detail

Route agent LLM traffic through Shepherd Model Gateway

Use Shepherd Model Gateway to route OpenAI, Anthropic, Responses API, embeddings, and MCP tool traffic across self-hosted and cloud model backends with cache-aware policies and observability.

Integrations & ConnectorsMulti-Framework
Integrations & Connectors Multi-Framework Security Reviewed
⭐ 380 GitHub stars
COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill route-agent-llm-traffic-through-shepherd-model-gateway Copy
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
At a glance
Tools required
SMG binary, Docker image, Python package, or Rust install; one or more model worker endpoints; agent runtime configured to call the gateway; optional Prometheus/OpenTelemetry stack
Install & setup
Install with docker pull lightseekorg/smg:latest, pip install smg, or cargo install smg. Start with smg launch –worker-urls http://localhost:8000, add additional worker URLs or –policy cache_aware as needed, then point OpenAI-compatible agent clients at the gateway endpoint.
Author
LightSeek contributors
Publisher
Open Source
Last updated
Jul 10, 2026
Quick brief

Use Shepherd Model Gateway when agent infrastructure needs one controlled endpoint for model traffic across vLLM, TensorRT-LLM, TokenSpeed, SGLang, Ollama, OpenAI-compatible servers, Anthropic, Gemini, Bedrock, Azure OpenAI, and other providers. Invoke it instead of wiring every agent directly to each model backend when the work needs cache-aware routing, failover, rate limits, tenant boundaries, Prometheus/OpenTelemetry visibility, chat history storage, WASM policy hooks, MCP tool execution, or OpenAI/Anthropic-compatible APIs from a single gateway. The workflow is to install SMG, launch it against one or more worker URLs, choose a routing policy, point agents at the gateway endpoint, and monitor request routing and failures. The scope boundary is model-gateway operations for agent runtimes, not a generic LLM platform or inference-engine listing.

How it works

What this skill actually does

Inputs and prerequisites: SMG binary, Docker image, Python package, or Rust install; one or more model worker endpoints; agent runtime configured to call the gateway; optional Prometheus/OpenTelemetry stack.

Setup notes: Install with docker pull lightseekorg/smg:latest, pip install smg, or cargo install smg. Start with smg launch –worker-urls http://localhost:8000, add additional worker URLs or –policy cache_aware as needed, then point OpenAI-compatible agent clients at the gateway endpoint.

Source and verification boundary: use https://lightseekorg.github.io/smg as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.

Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.